Eye State Detection Using Frequency Features from 1 or 2-Channel EEG
Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This pape...
Gespeichert in:
Veröffentlicht in: | International journal of neural systems 2023-12, Vol.33 (12) |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 12 |
container_start_page | |
container_title | International journal of neural systems |
container_volume | 33 |
creator | Laport, Francisco Dapena, Adriana Castro, Paula M. Iglesias, Daniel I. Vazquez-Araujo, Francisco J. |
description | Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states. |
doi_str_mv | 10.1142/S0129065723500624 |
format | Article |
fullrecord | <record><control><sourceid>proquest_ADCHV</sourceid><recordid>TN_cdi_worldscientific_primary_S0129065723500624</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3165619523</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3424-33847badbd5b627a1072a6877003a5398e4f3c88e72870ba3c9082145a71fa313</originalsourceid><addsrcrecordid>eNplkE9LAzEUxIMoWKsfwFvA8-p7-b9HabdVKHioPS_ZNKtb2k1NUqTf3i0VL57mMPN7bxhC7hEeEQV7WgKyEpTUjEsAxcQFGaEueaGEYpdkdLKLk39NblLaAKDQwozItDp6usw2ezr12bvchZ6uUtd_0Fn0XwffuyOdeZsP0SfaxrCjSEOkrJh82r73W1pV81ty1dpt8ne_OiarWfU-eSkWb_PXyfOicFwwUXBuhG7sulnLRjFtETSzymgNwK3kpfGi5c4Yr5nR0FjuSjAMhbQaW8uRj8nD-e4-hqFayvUmHGI_vKw5KqmwlIwPKTynXAwpRd_W-9jtbDzWCPVprPrfWAMDZ-Y7xO06uc73uWs794f-R34A6Ppn9g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3165619523</pqid></control><display><type>article</type><title>Eye State Detection Using Frequency Features from 1 or 2-Channel EEG</title><source>World Scientific Open</source><creator>Laport, Francisco ; Dapena, Adriana ; Castro, Paula M. ; Iglesias, Daniel I. ; Vazquez-Araujo, Francisco J.</creator><creatorcontrib>Laport, Francisco ; Dapena, Adriana ; Castro, Paula M. ; Iglesias, Daniel I. ; Vazquez-Araujo, Francisco J.</creatorcontrib><description>Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.</description><identifier>ISSN: 0129-0657</identifier><identifier>EISSN: 1793-6462</identifier><identifier>DOI: 10.1142/S0129065723500624</identifier><language>eng</language><publisher>Singapore: World Scientific Publishing Company</publisher><subject>Algorithms ; Channels ; Cost effectiveness ; Decision trees ; Discriminant analysis ; Electroencephalography ; Feature extraction ; Fourier transforms ; Hardware ; Human-computer interface ; Robustness ; Support vector machines</subject><ispartof>International journal of neural systems, 2023-12, Vol.33 (12)</ispartof><rights>2023, The Author(s)</rights><rights>2023. The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3424-33847badbd5b627a1072a6877003a5398e4f3c88e72870ba3c9082145a71fa313</cites><orcidid>0000-0001-7362-6854 ; 0000-0002-0521-3465 ; 0000-0002-6543-8236 ; 0000-0001-5830-681X ; 0000-0002-9964-5727</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.worldscientific.com/doi/reader/10.1142/S0129065723500624$$EPDF$$P50$$Gworldscientific$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,27474,27901,27902,55544</link.rule.ids><linktorsrc>$$Uhttp://dx.doi.org/10.1142/S0129065723500624$$EView_record_in_World_Scientific_Publishing$$FView_record_in_$$GWorld_Scientific_Publishing$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Laport, Francisco</creatorcontrib><creatorcontrib>Dapena, Adriana</creatorcontrib><creatorcontrib>Castro, Paula M.</creatorcontrib><creatorcontrib>Iglesias, Daniel I.</creatorcontrib><creatorcontrib>Vazquez-Araujo, Francisco J.</creatorcontrib><title>Eye State Detection Using Frequency Features from 1 or 2-Channel EEG</title><title>International journal of neural systems</title><description>Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.</description><subject>Algorithms</subject><subject>Channels</subject><subject>Cost effectiveness</subject><subject>Decision trees</subject><subject>Discriminant analysis</subject><subject>Electroencephalography</subject><subject>Feature extraction</subject><subject>Fourier transforms</subject><subject>Hardware</subject><subject>Human-computer interface</subject><subject>Robustness</subject><subject>Support vector machines</subject><issn>0129-0657</issn><issn>1793-6462</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ADCHV</sourceid><recordid>eNplkE9LAzEUxIMoWKsfwFvA8-p7-b9HabdVKHioPS_ZNKtb2k1NUqTf3i0VL57mMPN7bxhC7hEeEQV7WgKyEpTUjEsAxcQFGaEueaGEYpdkdLKLk39NblLaAKDQwozItDp6usw2ezr12bvchZ6uUtd_0Fn0XwffuyOdeZsP0SfaxrCjSEOkrJh82r73W1pV81ty1dpt8ne_OiarWfU-eSkWb_PXyfOicFwwUXBuhG7sulnLRjFtETSzymgNwK3kpfGi5c4Yr5nR0FjuSjAMhbQaW8uRj8nD-e4-hqFayvUmHGI_vKw5KqmwlIwPKTynXAwpRd_W-9jtbDzWCPVprPrfWAMDZ-Y7xO06uc73uWs794f-R34A6Ppn9g</recordid><startdate>202312</startdate><enddate>202312</enddate><creator>Laport, Francisco</creator><creator>Dapena, Adriana</creator><creator>Castro, Paula M.</creator><creator>Iglesias, Daniel I.</creator><creator>Vazquez-Araujo, Francisco J.</creator><general>World Scientific Publishing Company</general><general>World Scientific Publishing Co. Pte., Ltd</general><scope>ADCHV</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-7362-6854</orcidid><orcidid>https://orcid.org/0000-0002-0521-3465</orcidid><orcidid>https://orcid.org/0000-0002-6543-8236</orcidid><orcidid>https://orcid.org/0000-0001-5830-681X</orcidid><orcidid>https://orcid.org/0000-0002-9964-5727</orcidid></search><sort><creationdate>202312</creationdate><title>Eye State Detection Using Frequency Features from 1 or 2-Channel EEG</title><author>Laport, Francisco ; Dapena, Adriana ; Castro, Paula M. ; Iglesias, Daniel I. ; Vazquez-Araujo, Francisco J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3424-33847badbd5b627a1072a6877003a5398e4f3c88e72870ba3c9082145a71fa313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Channels</topic><topic>Cost effectiveness</topic><topic>Decision trees</topic><topic>Discriminant analysis</topic><topic>Electroencephalography</topic><topic>Feature extraction</topic><topic>Fourier transforms</topic><topic>Hardware</topic><topic>Human-computer interface</topic><topic>Robustness</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Laport, Francisco</creatorcontrib><creatorcontrib>Dapena, Adriana</creatorcontrib><creatorcontrib>Castro, Paula M.</creatorcontrib><creatorcontrib>Iglesias, Daniel I.</creatorcontrib><creatorcontrib>Vazquez-Araujo, Francisco J.</creatorcontrib><collection>World Scientific Open</collection><collection>CrossRef</collection><jtitle>International journal of neural systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Laport, Francisco</au><au>Dapena, Adriana</au><au>Castro, Paula M.</au><au>Iglesias, Daniel I.</au><au>Vazquez-Araujo, Francisco J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Eye State Detection Using Frequency Features from 1 or 2-Channel EEG</atitle><jtitle>International journal of neural systems</jtitle><date>2023-12</date><risdate>2023</risdate><volume>33</volume><issue>12</issue><issn>0129-0657</issn><eissn>1793-6462</eissn><abstract>Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.</abstract><cop>Singapore</cop><pub>World Scientific Publishing Company</pub><doi>10.1142/S0129065723500624</doi><orcidid>https://orcid.org/0000-0001-7362-6854</orcidid><orcidid>https://orcid.org/0000-0002-0521-3465</orcidid><orcidid>https://orcid.org/0000-0002-6543-8236</orcidid><orcidid>https://orcid.org/0000-0001-5830-681X</orcidid><orcidid>https://orcid.org/0000-0002-9964-5727</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 0129-0657 |
ispartof | International journal of neural systems, 2023-12, Vol.33 (12) |
issn | 0129-0657 1793-6462 |
language | eng |
recordid | cdi_worldscientific_primary_S0129065723500624 |
source | World Scientific Open |
subjects | Algorithms Channels Cost effectiveness Decision trees Discriminant analysis Electroencephalography Feature extraction Fourier transforms Hardware Human-computer interface Robustness Support vector machines |
title | Eye State Detection Using Frequency Features from 1 or 2-Channel EEG |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T15%3A35%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ADCHV&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Eye%20State%20Detection%20Using%20Frequency%20Features%20from%201%20or%202-Channel%20EEG&rft.jtitle=International%20journal%20of%20neural%20systems&rft.au=Laport,%20Francisco&rft.date=2023-12&rft.volume=33&rft.issue=12&rft.issn=0129-0657&rft.eissn=1793-6462&rft_id=info:doi/10.1142/S0129065723500624&rft_dat=%3Cproquest_ADCHV%3E3165619523%3C/proquest_ADCHV%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3165619523&rft_id=info:pmid/&rfr_iscdi=true |